This work describes the development of a low cost thermal desorption system to analyze a set of 31 exhaledbreath samples previously acquired from CA and control patients (i.e. with gastritis and ulcer), which wereconcentrated using Tenax tubes in order to remove the moisture and trap the volatile compounds. Thesamples were stored at a temperature of 4°C for further analysis. The proposed system allowed that thevolatile compounds were trapped inside the tubes to be extracted and sent to a measuring chamber with a gassensor array sensitive to these compounds. The overall detection system composed of the measuringchamber, a high-precision power supply, advanced high-resolution data acquisition equipment and a computerthat acquired and supervised the sensor responses. Once the information was acquired, different preprocessing(normalization) and data processing techniques such as: Principal Component Analysis (PCA),Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), were applied for the analysis anddata classification of the exhaled breath. The thermal desorption system was able to extract the volatilecompounds emitted from the breath, reducing the humidity of the samples to increase the selectivity,sensitivity and the performance of the system. A 99,44 % of the total variance by using PCA analysis wasachieved and a 93.54 % of classification success rate using SVM was obtained.
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